Preoperative Controlling Nutritional Status(CONUT) as an optimal prognostic nutritional index to predict the long-term outcome after radical surgery for colorectal cancer

doi:10.21203/rs.3.rs-3140994/v1

Download PDF

Research Article

Preoperative Controlling Nutritional Status(CONUT) as an optimal prognostic nutritional index to predict the long-term outcome after radical surgery for colorectal cancer

https://doi.org/10.21203/rs.3.rs-3140994/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Controlled nutritional status (CONUT) can effectively reflect the nutritional status of the body, which is closely related to cancer prognosis. The purpose of this study was to investigate the relationship between the CONUT score and prognosis after radical surgery for colorectal cancer, and to compare the predictive ability of the CONUT score with other indexes.

Methods

A retrospective analysis included 217 patients with newly diagnosed colorectal cancer was performed. The CONUT score was calculated based on the serum albumin level, total lymphocyte count, and total cholesterol level. The cutoff value of the CONUT score for predicting prognosis was 4 according to the Youden Index by Receiver operating characteristic curve. The associations between the CONUT score and the prognosis were performed by Kaplan-Meier curves and Cox regression analysis.

Results

Stratified by the cutoff value of the CONUT score, patients were divided into CONUT LOW group(n = 189) and CONUT HIGH group (n = 28). CONUT HIGH group had worse overall survival(p = 0.013) and Replase-free survival(P = 0.015). The predictive performances of CONUT were superior to NLR, mGPS and PNI. Meanwhile, the predictive performances of CONUT + TNM stage for 3-year OS (AUC = 0.803) and 3-year RFS (AUC = 0.765) were no less than SMI + TNM stage. The CONUT score was negatively correlated with SMI(p<0.01).

Conclusions

The CONUT score as a nutritional indicator could predict long-term outcome after radical surgery for colorectal cancer, and its predictive ability was superior to other indexes. The correlation between the CONUT score and skeletal muscle may be one of the factors that play a predictive role.

CONUT

Colorectal cancer

Prognostic marker

Sarcopenia

Skeletal muscle

Colorectal cancer is the third most common cancer in the world and ranks second in cancer-related deaths despite improvements in its treatment ^1,2. Therefore, it is warranted to identify effective tools in prognostic prediction for colorectal cancer. Especially after colorectal cancer radical surgery. Early risk stratification contributes to individualized treatment and overall prognosis..

It was proved by several types of researches that the decline in nutritional status makes it difficult for cancer patients to tolerate and recover after surgery, thus leading to poor prognosis^3,4. The decline of nutritional status will lead to the disorder of net protein balance in the body, and the protein synthesis is less than protein decomposition, thus affecting the muscle content of the body⁵. When the muscle content reduced to a certain extent, it can be defined as sarcopenia. Sarcopenia has been confirmed as an independent risk factor for multiple cancers by several studies ^6–9. Currently, sarcopenia is mainly diagnosed by CT of the third vertebra slice. But CT method of skeletal muscle measurement has many drawbacks, including high expense, radiation exposure, and sophisticated manipulation, which largely restricted its wide application. Therefore, a series of nutrition-related scoring tools, such as CONUT, mGPS, PNI, NLR, etc., had been developed in replacement of muscle measurement depending on serological indicators. Moreover, relevant studies have shown that these nutrition-related scores can be used to evaluate the prognosis of patients with colorectal cancer ^10–13. However, their predictive performance was varied between studies. Therefore, we need a convenient and relatively accurate nutrition-related scoring tool to evaluate the prognosis of patients with colorectal cancer.

As a new nutritional scoring tool proposed in recent years, the Controlling Nutritional Status (CONUT) score has been proven effective in assessing the nutritional status of patients¹⁴. The calculation of the CONUT score was based on serum albumin level, total peripheral lymphocyte counts, and total cholesterol level as previously described ¹⁵. These serum indicators are readily available and convenient for clinical evaluation. According to recent studies, the CONUT score can be used to predict the outcome of a variety of cancers, such as gastric cancer¹⁶, urinary system cancer¹⁷, and lung cancer¹⁸. Nevertheless, the value of CONUT score in predicting long-term prognosis after radical resection of colorectal cancer has not been clearly reported. Compared with other nutrition-related scoring tools that rely on serological indicators, the accuracy of its predictive ability and how to exert its predictive ability are also unclear.

This study mainly aimed to analyze the predict performance of the CONUT score on survival rate of colorectal cancer patients who underwent potentially curative resection. Meanwhile, we also compared the prognostic ability of the CONUT score with other nutritional scoring systems in predicting the long-term outcome after radical surgery for colorectal cancer, and explored how the CONUT score exerted its predictive power.

2.1 Patients

A database comprised of 373 patients newly diagnosed of colorectal cancer and underwent surgery operation either in Nanjing Drum Tower Hospital or General Hospital Of Eastern Theater Command between January 2015 and January 2019 was analyzed retrospectively. Patients with pre-operative neoadjuvant chemotherapy, absent of pre-operative Computed Tomography (CT) images, TNM stage IV, incomplete medical records, or lost during routine follow-up were excluded. Finally, 217 patients were included in this study. This observational study was approved by the Clinical Research Ethics Committee of Nanjing Drum Tower Hospital and General Hospital of Eastern Theater Command. Written informed consent was obtained.

Laboratory tests were based on fasting blood collected on the morning of the first day of admission. Comorbidities included tuberculosis, hepatitis, diabetes, hypertension, coronary heart disease, hyperlipidemia, and other chronic diseases. Postoperative complications were defined as intestinal obstruction, intestinal paralysis, anastomotic leakage, abdominal infection, and other diseases during the perioperative period.

2.2 The CONUT score

The CONUT score was calculated as previously described and illustrated in Table 1 ¹⁴. The CONUT consists of scores of serum albumin, lymphocyte counts, and total cholesterol. The CONUT score ≥ 5 was considered a moderate to severe malnutrition state.

2.3 Skeletal muscle mass index(SMI)

The skeletal muscle area (SMA) was measured using ImageJ software to measure the areas of the psoas major, paraspinal muscles, transverse abdominis, external oblique, internal oblique, and rectus abdominis in the cross-section of the third lumbar vertebrae on CT¹⁹( Fig. 1). Skeletal muscle mass index(SMI) was calculated as dividing SMA (cm2) by the square of height (m). Sarcopenia was defined as SMI less than 40.8 (cm2/m2) in men and SMI less than 34.9 (cm2/m2) in women⁸.

2.4 Other nutritional scoring systems

The neutrophil-to-lymphocyte ratio(NLR) was calculated as the ratio of pre-operative neutrophil count and lymphocyte counts¹². The modified Glasgow prognostic score(mGPS) was scored according to pre-operative C-reactive protein and albumin. Patients were divided into three categories by mGPS: 0(C-Reactive protein ≤ 10 mg/L and albumin ≥ 35 g/L), 1(C-Reactive protein > 10 mg/L or albumin<35 g/L ) and 2(C-Reactive protein > 10 mg/L and albumin<35 g/L )¹⁰. The prognostic nutritional index(PNI) was calculated based on preoperative albumin and lymphocyte counts: 10×albumin (g/dl) + 0.005×total lymphocyte counts (per mm3)²⁰. The scores listed above were nutritional evaluation systems and were suggested to be useful in cancer prognostic risk stratification^10–13.

2.5 Follow up

Patients who underwent colorectal cancer surgery and was discharged from the hospital were followed by telephone every three months. Regular return and follow-up at the hospital were also required. The primary endpoint was death from all causes of illness. The secondary endpoint was medically proven recurrence.

2.6 Statistical analysis

IBM SPSS Statistics for Windows, Version 26.0(Armonk, NY: IBM Corp) was used for statistical analysis. The correlations between parameters were examined using Pearson's or Spearman's correlation coefficient when appropriate. Differences between groups were assessed by χ2 test or ANOVA. The Kaplan-Meier curve was used to analyze 3-year overall survival (OS) and 3-year recurrence-free survival (RFS). COX proportional hazards models were utilized to identify variables with significant independent associations with OS and RFS. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the accuracy of prognosis prediction and was compared by the DeLong test between different scores. P < 0.05 was considered significantly different.

3.1 Clinical characteristics

Among the 217 patients with colorectal cancer included, 92 (42.4%) were male, and 125 (57.6%) were female, with a mean age of 58.4 years (range 24–88 years). Based on the time-dependent ROC, the best cutoff value of the CONUT score for predicting 3-year overall survival was 4. Therefore, patients were divided into CONUT LOW group (CONUT ≤ 4) (n = 189, 87.1%) and CONUT HIGH group (CONUT ≥ 5)(n = 28, 12.9%). Variables between the two groups were compared.

The Age, sex, tumor site, rates of comorbidities, pre-operative white blood cell count, platelet count, serum CEA and CA199 levels between the two groups were not significantly different. Patients in CONUT HIGH group showed a later tumor TNM stage(p = 0.044). Patients with higher CONUT scores had a higher incidence of postoperative complication rates (p = 0.044). They also had lower BMI, lower hemoglobin level, higher C-reactive protein (CRP) level, lower albumin levelsand lower cholesterol levels The incidence of sarcopenia in CONUT HIGH group was also higher. The results indicated a higher inflammatory state and poorer protein reserve. (Table 2)

Table 2

Clinicopathological characteristics of 217 patients with colorectal cancer after surgery.
	T	The CONUT score
Variable	ALL(217)	CONUT LOW(189)	CONUT HIGH(28)	P value
Age (years)	58.4 ± 12.7	58.4 ± 12.4	58.9 ± 15.1	0.842
Male sex, n (%)	125(57.6%)	113(59.8%)	12(42.9%)	0.091
BMI (kg/m2)	22.9 ± 3.2	23.1 ± 3.1	21.5 ± 3.9	0.015
Tumor site, n (%)				0.787
Colon	122(56.2%)	105(55.6%)	17(60.7%)
Rectum	95(43.8%)	84(44.4%)	11(39.3%)
Comorbidities, yes, n (%)	69(31.8%)	60(31.7%)	9(32.1%)	0.966
Sarcopenia, yes, n (%)	49(22.6%)	32(16.9%)	17(60.7%)	<0.01
WBC (×10⁹/L)	5.84 ± 1.92	5.92 ± 1.92	5.27 ± 1.86	0.093
Platelet (×10⁹/L)	218 ± 85	222 ± 89	191 ± 46	0.068
Hemoglobin (g/L)	122.5 ± 24.6	125.6 ± 23.2	101.6 ± 24.1	<0.01
Albumin (g/L)	41.3 ± 4.4	42.1 ± 3.7	35.7 ± 4.5	<0.01
Lymphocyte (×10⁹/L)	1.6 ± 0.7	1.7 ± 0.6	0.9 ± 0.3	<0.01
Cholesterol (mg/dL)	166.6 ± 41.5	173.0 ± 39.2	123.7 ± 30.0	<0.01
CRP (mg/L)	8.9 ± 20.3	7.5 ± 19.7	18.5 ± 21.9	0.007
CEA (ng/ml)	5.9 ± 10.1	5.9 ± 10.5	6.6 ± 7.1	0.706
CA199 (U/ml)	25.5 ± 55.4	25.7 ± 56.8	24.5 ± 45.6	0.920
Postoperative complications, yes, n (%)	34(15.7%)	26(13.8%)	8(28.6%)	0.044
TNM stage, n (%)				0.044
Ⅰ	36(6.6%)	35(18.5%)	1(3.6%)
Ⅱ	91(1.9%)	74(39.2%)	17(60.7%)
Ⅲ	90(41.5%)	80(42.3%)	10(35.7%)
CONUT, Controlling Nutritional Status; BMI, body mass index; WBC, white blood cell; CEA, carcinoembryonic antigen; CA199, Carbohydrate antigen199; CRP, C-reactive protein;

3.2 CONUT and survival

Median follow-up was 49.6 months (ranged 8–85 months). The 3-year OS (94.1% vs. 71.4%, p<0.01) and RFS (89.9% vs. 64.2%, p<0.01) of the CONUT LOW group were significantly higher than those in the CONUT HIGH group. Due to the high mortality and recurrence rate of colon cancer in stage III patients, we further analyzed stage III patients in order to better explore the relationship between CONUT score and patient prognosis. In patients with TNM stage III, the 3-year OS and RFS in the CONUT LOW were also significantly higher than those in the CONUT HIGH group(90% vs 40%, p<0.01; 83.7% vs. 40%, p<0.01 ).There was significant difference in 3-year RFS between CONUT LOW and HIGH in patients with TNM stage I-II (p<0.01), but not with 3-year OS (P = 0.08)(Fig. 2). This may due to the fact of lower mortality and the follow up period was relatively short for in stage I-II.

3.3 Cox regression analysis

Baseline factors without linear inter-correlations were included in univariate Cox regression analysis. Factors that had statistically significance were included in multivariate analysis. In multivariate analysis, sarcopenia (HR = 3.640, 95%CI: 1.182–11.207, P = 0.024), TNM tumor stage ( Ⅲ vs. Ⅱ, Ⅰ HR = 4.396, 95%CI: 1.534–12.598, P = 0.006) and the CONUT socre(HR = 3.920, 95%CI: 1.330-11.547, P = 0.013) were finally confirmed associated with 3-year OS(Table 3). Sarcopenia (HR = 4.122, 95%CI: 1.678–10.125, P = 0.002), TNM tumor stage( Ⅲ vs. Ⅱ, Ⅰ HR = 3.603, 95%CI: 1.612–8.053, P = 0.002), and the CONUT score (HR = 2.992, 95%CI: 1.241–7.212, P = 0.015) were associated with 3-year RFS(Table 4).

Table 3

Uni- and multivariate analyses of factors associated with 3-year overall survival (OS) in patients with colorectal cancer
		Univariate analysis			Multivariate analysis
Variable		HR	95%CI	P value	HR	95%CI	P value
Age (years)	≥ 65/<65	1.815	0.738–4.468	0.194
Sex	Female/Male	1.577	0.641–3.881	0.321
BMI (kg/m2)	≥ 24/ <24	0.202	0.047–0.873	0.032	0.506	0.103–2.474	0.400
Tumor site	Colon/Rectum	0.931	0.374–2.314	0.877
Comorbidities	Yes/NO	2.557	0.745–8.777	0.136
Sarcopenia	Yes/NO	8.012	3.151–20.372	<0.01	3.640	1.182–11.207	0.024
WBC (×10⁹/L)	≥ 10/<10	1.228	0.164–9.202	0.841
Platelet (×10⁹/L)	≥ 300/<300	0.798	0.184–3.454	0.763
Hemoglobin (g/L)	≥ 90/<90	0.367	0.132–1.019	0.054	0.696	0.234–2.071	0.515
CRP (mg/L)	> 10/≤10	1.647	0.381–7.130	0.504
CEA (ng/ml)	≥ 3.4/<3.4	2.319	0.881–6.101	0.088	1.970	0.708–5.480	0.194
CA199 (U/ml)	≥ 37/<37	1.903	0.254–14.257	0.531
Postoperative complications	Yes/NO	1.028	0.300-3.529	0.965
TNM stage	Ⅲ/Ⅰ,Ⅱ	4.167	1.501–11.571	0.006	4.396	1.534–12.598	0.006
CONUT score	≥ 5/≤4	5.939	2.386–14.785	<0.01	3.920	1.330-11.547	0.013
BMI, body mass index; WBC, white blood cell; CEA, carcinoembryonic antigen; CA199,Carbohydrate antigen199; CONUT, Controlling Nutritional Status; HB, hemoglobin; CRP, C-reactive protein.

Table 4

Uni- and multivariate analyses of factors associated with 3-year replase-free survival (RFS) rates in patients with colorectal cancer
		Univariate analysis			Multivariate analysis
Variable		HR	95%CI	P value	HR	95%CI	P value
Age (years)	≥ 65/<65	1.625	0.781–3.380	0.194
Sex	Female/Male	1.503	0.724–3.118	0.274
BMI (kg/m2)	≥ 24/ <24	0.354	0.135–0.928	0.035	0.792	0.269–2.337	0.673
Tumor site	Colon/Rectum	0.986	0.469–2.074	0.970
Comorbidities	Yes/NO	1.462	0.624–3.428	0.382
Sarcopenia	Yes/NO	6.795	3.240–14.250	<0.01	4.122	1.678–10.125	0.002
WBC (×10⁹/L)	≥ 10/<10	1.728	0.410–7.283	0.490
Platelet (×10⁹/L)	≥ 300/<300	1.294	0.392–4.276	0.673
Hemoglobin (g/L)	≥ 90/<90	0.492	0.199–1.214	0.124
CRP (mg/L)	> 10/≤10	1.625	0.491–5.382	0.427
CEA (ng/ml)	≥ 3.4/<3.4	2.356	1.072–5.177	0.033	2.220	0.989–4.984	0.053
CA199 (U/ml)	≥ 37/<37	1.483	0.352–6.243	0.591
Postoperative complications	Yes/NO	1.028	0.300-3.529	0.965
TNM stage	Ⅲ/Ⅰ,Ⅱ	3.477	1.581–7.650	0.002	3.603	1.612–8.053	0.002
CONUT score	≥ 5/≤4	4.724	2.177–10.254	<0.01	2.992	1.241–7.212	0.015
BMI, body mass index; WBC, white blood cell; CEA, carcinoembryonic antigen; CA199,Carbohydrate antigen199; CONUT, Controlling Nutritional Status; HB, hemoglobin; CRP, C-reactive protein.

3.4 Comparison of the predictive accuracy between nutritional scores

Time-dependent ROCs were utilized to predict 3-year OS in overall patients. The area under ROCs (AUROC) of CONUT, NLR, mGPS, and PNI were 0.728 (95%CI: 0.607–0.850, P<0.01 ), 0.585 (95%CI: 0.433–0.736, P = 0.222), 0.585 (95%CI: 0.445–0.726, P = 0.219) and 0.414 (95%CI: 0.262–0.566, P = 0.215), respectively(Fig. 3). The CONUT score showed higher accuracy than NLR, mGPS, and PNI for predicting the 3-year OS in overall patients. The predictive accuracy was significantly different between CONUT and other scores (all p<0.05) by the DeLong test. The best cutoff value of CONUT in predicting 3-year OS was four as mentioned above. The CONUT score(AUC = 0.662, 95%CI: 0.546–0.778, p = 0.005 ) was also more effective in predicting the 3-year RFS in general patients enrolled (Fig. 3).

3.5 Comparison and correlation between the CONUT score and Skeletal muscle mass index

Time-dependent ROCs were utilized to predict 3-year OS in overall patients. The AUCROC of CONUT + TNM stage(AUC = 0.803, 95%CI: 0.703–0.904, P<0.01) were higher than SMI + TNM stage(AUC = 0.760, 95%CI: 0.624–0.896,P<0.01). The same conclusion was also found in the prediction of 3-year RFS. Spearman correlation analysis was used to evaluate the correlations between the CONUT score and SMI. The CONUT score was negatively correlated with SMI(Fig. 4). The relationships indicated that CONUT score could reflect the body SMI to a certain extent and can replace SMI as a postoperative prognostic marker for colorectal cancer.

This study mainly explored the associations between preoperative CONUT score and tumor prognosis among colorectal cancer patients. The pre-operative CONUT ≥ 5 was independently correlated with a 3.92-fold of increased risk of poor OS and a 2.99-fold increased risk of poor RFS. By comparison, we found that the predictive power of the CONUT score was more accurate than that of other nutritional indexes. In addition, we also found that the CONUT score was in paralleled with the body skeletal muscle index, which strongly represented body protein reserve and nutritional state. Therefore, the CONUT score may serve as an optimal prognostic nutritional index to predict the long-term outcome after radical surgery for colorectal cancer.

In recent years, there are many researches on nutritional status and tumor prognosis. The level of skeletal muscle mass was currently considered as the optimal indicator of nutritional status of patients. Jingjie Xiao ⁹ et al. found that preoperative skeletal muscle level could predict tumor prognosis. As reported, those with a pre-operative diagnosis of sarcopenia had a 1.1-1.2-fold higher incidence of postoperative complications, 2-fold increased risk of death within 30 days after surgery than those without sarcopenia among colorectal cancer patients ^6–9. Nonetheless, skeletal muscle measurement method is not widely promoted because of its consumables, time consuming, power consuming, insecurity and other disadvantages. Therefore, to identify a relatively convenient and safe early prediction indicator was warranted. Although Allan M. Golder²¹ and Nozoe T²² et al. found that mGPS, NLR and PNI indexes could serve as prediction tools for prognosis in multiple cancers, the predictive power of such indicators remained unsatisfactory. In our study, the accuracy of the CONU score was higher than that of mGPS, NLR and PNI. Consequently, the CONUT score showed satisfactory prediction performance. This objective, safe, simple and easy, accessible tool was more conducive to early and rapid prediction and has clinical application value.

As a nutritional status score, the effective role of the CONUT score has been clinically proven to evaluate nutrition¹⁴. The predictive ability of CONUT to predict the prognosis of colon cancer may be related to its three components. Firstly, the serum albumin was a biomarker for systemic inflammation. Tumor prognosis was highly correlated with inflammation state. Excessive inflammatory activation affected the tumor microenvironment ²³ and suppressed normal immune reactions ²⁴. Secondly, Zhang YY²⁵, Zhao J²⁶, et al. demonstrated that colon cancer patients with reduced lymphocyte counts had worse prognosis than patients with normal lymphocyte counts. Lymphocytes, as immune cells of the human body, could recognize and kill tumor cells through humoral and cellular immunity pathways, thus playing a significant role in inhibiting tumor cell proliferation and metastasis²⁷. Thirdly, cholesterol is mainly synthesized in the liver and exists in blood in the form of low-density lipoprotein (LDL). LDL receptor (LDLR) was expressed on the surface of normal cells and tumor cells. Through the binding of LDLR to LDL, the LDL in serum enters cells through endocytosis²⁸. Different from normal tissues, LDLR could not limit intracellular cholesterol transportation through feedback regulation ²⁹. Therefore, it was unrestricted for tumor cells to transport cholesterol into cells to provide energy. Moreover, cancer cells could also use the metabolites produced by cholesterol metabolism to synthesize substances that can promote their growth and development ^30,31. Therefore, low serum cholesterol level could be the result of an increased risk of cancer, indicating a poor prognosis. In conclusion, The ability of the CONUT score to predict prognosis may be due to its comprehensive reflection of the body's nutritional status, inflammatory status, immune status and tumor metabolism.

Besides, our study also found a correlation between CONUT score and skeletal muscle level. (R2 = 0.137, correlation coefficient − 0.319, p<0.01), possibly because both scores reflect the protein reserve of the body. Patients with cancer, especially those who underwent surgery procedures, could experience larger demand for energy and protein intake than normal people ³². Inadequate energy or protein intake would accelerate the glycogenolysis process leading to glycogen depletion, further promoting gluconeogenesis and leading to body skeletal protein decomposition³³. Patients would be unable to tolerate surgery, delay recovery, and even more prone to tumor recurrence, resulting in a poor prognosis when protein reserver was insufficient. This may also be one of the factors contributing to the predictive power of CONUT scores.

The present study had several limitations. First, this study was a retrospective single-center study comprised of patients from the same region, which was homogeneous in a race. Second, the sample size was small and the follow-up time was not long enough. Last, the post-operative CONUT was not followed up and the nutritional status of patients could not be assessed dynamically. Therefore, prospective studies are warranted further to confirm the predictive significance of the CONUT score.

To conclude, the CONUT score was significantly associated with the postoperative prognosis of colorectal cancer patients. Compared with other nutritional scores, CONUT score could serve as an optimal prognostic nutritional index to predict the long-term outcome after radical surgery for colorectal cancer. In addition, the predictive power of CONUT score may be related to skeletal muscle.

Availability of Data and Materials

The data used during this study are in the excel sheet of patient data.

Author Contributions

Study conception and design: WG and CD. Collection of date: LL, QY and SZ. Analysing of data: LL, QY, SZ, and TX. Drafting of the manuscript: LL. Revising of manuscript: JM, CD, and WG. All authors read and approved the final manuscript.

Acknowledgments The authors thank all the investigators who participated in the study.

Funds

This work was supported by the National Natural Science Foundation of China (82172149) .

Ethics approval and consent to participate

The entire process followed the ethical standards of the Declaration of Helsinki. This study was approved by the ethics committee of Nanjing Drum Tower Hospital and General Hospital of Eastern Theater Command, and was performed after informed consent was.

Competing Interests The authors declare that there is no conflict of interests in this study.

Biller LH, Schrag D. Diagnosis and Treatment of Metastatic Colorectal Cancer: A Review. JAMA. Feb 16 2021;325(7):669–685.
Fan A, Wang B, Wang X, et al. Immunotherapy in colorectal cancer: current achievements and future perspective. Int J Biol Sci. 2021;17(14):3837–3849.
Haraga J, Nakamura K, Omichi C, et al. Pretreatment prognostic nutritional index is a significant predictor of prognosis in patients with cervical cancer treated with concurrent chemoradiotherapy. Mol Clin Oncol. Nov 2016;5(5):567–574.
Thanikachalam K, Khan G. Colorectal Cancer and Nutrition. Nutrients. Jan 14 2019;11(1).
Deldicque L. Protein Intake and Exercise-Induced Skeletal Muscle Hypertrophy: An Update. Nutrients. Jul 7 2020;12(7).
Hopkins JJ, Reif RL, Bigam DL, Baracos VE, Eurich DT, Sawyer MB. The Impact of Muscle and Adipose Tissue on Long-term Survival in Patients With Stage I to III Colorectal Cancer. Dis Colon Rectum. May 2019;62(5):549–560.
Olmez T, Karakose E, Bozkurt H, et al. Sarcopenia is associated with increased severe postoperative complications after colon cancer surgery. Arch Med Sci. 2021;17(2):361–367.
Su H, Ruan J, Chen T, Lin E, Shi L. CT-assessed sarcopenia is a predictive factor for both long-term and short-term outcomes in gastrointestinal oncology patients: a systematic review and meta-analysis. Cancer Imaging. Dec 3 2019;19(1):82.
Xiao J, Caan BJ, Cespedes Feliciano EM, et al. Association of Low Muscle Mass and Low Muscle Radiodensity With Morbidity and Mortality for Colon Cancer Surgery. JAMA Surg. Oct 1 2020;155(10):942–949.
Hirashima K, Watanabe M, Shigaki H, et al. Prognostic significance of the modified Glasgow prognostic score in elderly patients with gastric cancer. J Gastroenterol. Jun 2014;49(6):1040–1046.
Mohri Y, Inoue Y, Tanaka K, Hiro J, Uchida K, Kusunoki M. Prognostic nutritional index predicts postoperative outcome in colorectal cancer. World journal of surgery. Nov 2013;37(11):2688–2692.
Rossi S, Basso M, Strippoli A, et al. Are Markers of Systemic Inflammation Good Prognostic Indicators in Colorectal Cancer? Clinical colorectal cancer. Dec 2017;16(4):264–274.
Yamamoto T, Kawada K, Obama K. Inflammation-Related Biomarkers for the Prediction of Prognosis in Colorectal Cancer Patients. International journal of molecular sciences. Jul 27 2021;22(15).
Ignacio de Ulíbarri J, González-Madroño A, de Villar NG, et al. CONUT: a tool for controlling nutritional status. First validation in a hospital population. Nutricion hospitalaria. Jan-Feb 2005;20(1):38–45.
Kuroda D, Sawayama H, Kurashige J, et al. Controlling Nutritional Status (CONUT) score is a prognostic marker for gastric cancer patients after curative resection. Gastric Cancer. Mar 2018;21(2):204–212.
Zhang Y, Zhang X. Controlling nutritional status score, a promising prognostic marker in patients with gastrointestinal cancers after surgery: A systematic review and meta-analysis. International journal of surgery (London, England). Jul 2018;55:39–45.
Niu X, Zhu Z, Bao J. Prognostic significance of pretreatment controlling nutritional status score in urological cancers: a systematic review and meta-analysis. Cancer cell international. Feb 19 2021;21(1):126.
Toyokawa G, Kozuma Y, Matsubara T, et al. Prognostic impact of controlling nutritional status score in resected lung squamous cell carcinoma. Journal of thoracic disease. Sep 2017;9(9):2942–2951.
Reisinger KW, van Vugt JL, Tegels JJ, et al. Functional compromise reflected by sarcopenia, frailty, and nutritional depletion predicts adverse postoperative outcome after colorectal cancer surgery. Annals of surgery. Feb 2015;261(2):345–352.
Yamamoto M, Saito H, Uejima C, et al. Combination of Serum Albumin and Cholinesterase Levels as Prognostic Indicator in Patients ith Colorectal Cancer. Anticancer research. Feb 2019;39(2):1085–1090.
Golder AM, McMillan DC, Park JH, Mansouri D, Horgan PG, Roxburgh CS. The prognostic value of combined measures of the systemic inflammatory response in patients with colon cancer: an analysis of 1700 patients. Br J Cancer. May 2021;124(11):1828–1835.
Nozoe T, Kohno M, Iguchi T, et al. The prognostic nutritional index can be a prognostic indicator in colorectal carcinoma. Surg Today. Jun 2012;42(6):532–535.
Khandia R, Munjal A. Interplay between inflammation and cancer. Adv Protein Chem Struct Biol. 2020;119:199–245.
Greten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity. Jul 16 2019;51(1):27–41.
Zhang YY, Li WQ, Li ZF, et al. Higher Levels of Pre-operative Peripheral Lymphocyte Count Is a Favorable Prognostic Factor for Patients With Stage I and II Rectal Cancer. Frontiers in oncology. 2019;9:960.
Zhao J, Huang W, Wu Y, et al. Prognostic role of pretreatment blood lymphocyte count in patients with solid tumors: a systematic review and meta-analysis. Cancer cell international. 2020;20:15.
Templeton AJ, McNamara MG, Seruga B, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst. Jun 2014;106(6):dju124.
Deng CF, Zhu N, Zhao TJ, et al. Involvement of LDL and ox-LDL in Cancer Development and Its Therapeutical Potential. Front Oncol. 2022;12:803473.
Lum DF, McQuaid KR, Gilbertson VL, Hughes-Fulford M. Coordinate up-regulation of low-density lipoprotein receptor and cyclo-oxygenase-2 gene expression in human colorectal cells and in colorectal adenocarcinoma biopsies. International journal of cancer. Oct 8 1999;83(2):162–166.
Chen Y, Hughes-Fulford M. Human prostate cancer cells lack feedback regulation of low-density lipoprotein receptor and its regulator, SREBP2. International journal of cancer. Jan 1 2001;91(1):41–45.
<Human prostate cancer cells lack feedback regulation of low-density lipoprotein receptor and its regulator, SREBP2..pdf>.
Lobo DN, Gianotti L, Adiamah A, et al. Perioperative nutrition: Recommendations from the ESPEN expert group. Clin Nutr. Nov 2020;39(11):3211–3227.
Deer RR, Volpi E. Protein intake and muscle function in older adults. Curr Opin Clin Nutr Metab Care. May 2015;18(3):248–253.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Preoperative Controlling Nutritional Status(CONUT) as an optimal prognostic nutritional index to predict the long-term outcome after radical surgery for colorectal cancer

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methods

2.1 Patients

2.2 The CONUT score

2.3 Skeletal muscle mass index(SMI)

2.4 Other nutritional scoring systems

2.5 Follow up

2.6 Statistical analysis

3. Results

3.1 Clinical characteristics

3.2 CONUT and survival

3.3 Cox regression analysis

3.4 Comparison of the predictive accuracy between nutritional scores

3.5 Comparison and correlation between the CONUT score and Skeletal muscle mass index

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1